[fix] Add tool call parser KimiK2RawIdDetector to keep original tool call id.

python/sglang/srt/entrypoints/openai/serving_chat.py

@@ -1138,11 +1138,7 @@ def _process_tool_call_id(
         history_tool_calls_cnt: int,
     ) -> str:
         """Process for generating a new and unique `tool_call_id`"""
-        if self.tool_call_parser != "kimi_k2":
-            # A simple uuid is sufficient for all models except for Kimi-K2.
-            tool_call_id = f"call_{uuid.uuid4().hex[:24]}"
-            return tool_call_id
-        else:
+        if self.tool_call_parser == "kimi_k2":
             # Align with Kimi-K2 format: functions.{name}:{index}
             # Kimi-K2 allows multiple tool_calls in one message; SGLang sets call_item.tool_index to the *local* position inside that message.
             # Therefore, the index must be corrected by using `history_tool_calls_cnt + call_item.tool_index` to ensure globally unique and properly ordered.
@@ -1151,6 +1147,17 @@ def _process_tool_call_id(
                 f"Process tool call idx, parser: {self.tool_call_parser}, tool_call_id: {tool_call_id}, history_cnt: {history_tool_calls_cnt}"
             )
             return tool_call_id
+        if self.tool_call_parser == "kimi_k2_raw_id":
+            # RL training needs the model-emitted tool_call_id round-tripped verbatim,
+            # so we skip the history-based renumbering above and return whatever the
+            # detector captured. Fall back to the canonical Kimi-K2 reconstruction
+            # (without history offset) if for any reason the detector did not record
+            # a raw id — the raw id field is best-effort but the format is stable.
+            if call_item.tool_call_id:
+                return call_item.tool_call_id
+            return f"functions.{call_item.name}:{call_item.tool_index}"
+        # A simple uuid is sufficient for all other models.
+        return f"call_{uuid.uuid4().hex[:24]}"
 
     def _process_tool_calls(
         self,

python/sglang/srt/function_call/core_types.py

@@ -10,6 +10,12 @@ class ToolCallItem(BaseModel):
     tool_index: int
     name: Optional[str] = None
     parameters: str  # JSON string
+    # The tool_call_id string emitted by the model, captured verbatim.
+    # Only populated by detectors whose downstream consumers need the exact
+    # model-emitted id (e.g. RL training trajectories). Existing detectors
+    # leave this as None and the serving layer falls back to its usual id
+    # generation strategy.
+    tool_call_id: Optional[str] = None
 
 
 class StreamingParseResult(BaseModel):

python/sglang/srt/function_call/function_call_parser.py

@@ -20,7 +20,10 @@
 from sglang.srt.function_call.gpt_oss_detector import GptOssDetector
 from sglang.srt.function_call.hermes_detector import HermesDetector
 from sglang.srt.function_call.internlm_detector import InternlmDetector
-from sglang.srt.function_call.kimik2_detector import KimiK2Detector
+from sglang.srt.function_call.kimik2_detector import (
+    KimiK2Detector,
+    KimiK2RawIdDetector,
+)
 from sglang.srt.function_call.lfm2_detector import Lfm2Detector
 from sglang.srt.function_call.llama32_detector import Llama32Detector
 from sglang.srt.function_call.mimo_detector import MiMoDetector
@@ -54,6 +57,7 @@ class FunctionCallParser:
         "glm47": Glm47MoeDetector,
         "gpt-oss": GptOssDetector,
         "kimi_k2": KimiK2Detector,
+        "kimi_k2_raw_id": KimiK2RawIdDetector,
         "lfm2": Lfm2Detector,
         "llama3": Llama32Detector,
         "mimo": MiMoDetector,

python/sglang/srt/function_call/kimik2_detector.py

@@ -113,6 +113,7 @@ def detect_and_parse(self, text: str, tools: List[Tool]) -> StreamingParseResult
                         tool_index=function_idx,
                         name=function_name,
                         parameters=function_args,
+                        tool_call_id=function_id,
                     )
                 )
 
@@ -177,6 +178,7 @@ def parse_streaming_increment(
                             tool_index=self.current_tool_id,
                             name=function_name,
                             parameters="",
+                            tool_call_id=function_id,
                         )
                     )
                     self.current_tool_name_sent = True
@@ -253,3 +255,23 @@ def get_info(name: str) -> StructureInfo:
             )
 
         return get_info
+
+
+class KimiK2RawIdDetector(KimiK2Detector):
+    """
+    Variant of KimiK2Detector that preserves the model-emitted tool_call_id verbatim.
+
+    The default kimi_k2 path renumbers ids via `history_tool_calls_cnt + tool_index`
+    in the serving layer so that multi-turn conversations get globally unique,
+    monotonically increasing ids (see PR #10600). That is the right behavior for
+    chat use cases.
+
+    RL training has the opposite requirement: the trajectory must round-trip the
+    exact tool_call_id the model produced (e.g. `functions.foo:5`), so that the
+    follow-up tool result turn references the same id the policy emitted. This
+    subclass exists purely as a marker so the serving layer can branch on the
+    parser name and use `ToolCallItem.tool_call_id` directly. Parsing logic
+    is identical to KimiK2Detector.
+    """
+
+    pass